Fast 3D Point-Cloud Segmentation for Interactive Surfaces

Easily accessible depth sensors have enabled using point-cloud data to augment tabletop surfaces in everyday environments. However, point-cloud operations are computationally expensive and challenging to perform in real-time, particularly when targeting embedded systems without a dedicated GPU. In this paper, we propose mitigating the high computational costs by segmenting candidate interaction regions near real-time. We contribute an open-source solution for variable depth cameras using CPU-based architectures. For validation, we employ Microsoft’s Azure Kinect and report achieved performance. Our initial findings show that our approach takes under to segment candidate interaction regions on a tabletop surface and reduces the data volume by up to 70%. We conclude by contrasting the performance of our solution against a model-fitting approach implemented by the SurfaceStreams toolkit. Our approach outperforms the RANSAC-based strategy within the context of our test scenario, segmenting a tabletop’s interaction region up to 94% faster. Our results show promise for point-cloud-based approaches, even when targeting embedded solutions with limited resources.